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Record W2148364797 · doi:10.1109/mwsym.1996.511039

A modified method-of-moments technique for the full-wave analysis of imperfect conductors on lossy and finite-extent substrates

2002· article· en· W2148364797 on OpenAlexaff
M. Gimersky, Jens Børnemann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMethod of moments (probability theory)Perfect conductorConductorLossy compressionElectrical conductorDielectricGround planeMaterials scienceBoundary value problemElectrical impedanceDielectric lossElectronic engineeringAcousticsOpticsMathematical analysisComputer scienceMathematicsPhysicsOptoelectronicsElectrical engineeringEngineeringComposite materialAntenna (radio)Telecommunications

Abstract

fetched live from OpenAlex

A modified method-of-moments technique with general field-solver capability is presented. The structure to be analyzed is subdivided into a number of thin-wall cells. Surface impedance concepts are used to represent the material characteristics of each cell. The outstanding advantages of this method include: the absence of absorbing boundary conditions, as material parameters are defined with respect to a surrounding environment, e.g., free-space, thus minimizing the computational domain; conductor and dielectric losses are readily incorporated via the surface impedance concept; and radiation into any direction, even below the ground-plane of a finite-extent substrate, is included. Several examples involving imperfect conductors as well as lossy and finite-extent dielectric substrates are presented. The method is compared with measured results and is found to be in good agreement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.288
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2002
Admission routes1
Has abstractyes

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